Islamic Azad University North Tehran Branch

About: Islamic Azad University North Tehran Branch is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Adsorption & Catalysis. The organization has 868 authors who have published 968 publications receiving 9987 citations.

Fuzzy Logic Assessment for Bullwhip Effect in Supply Chain

Abstract: Having an efficient supply chain has always been a challenge for managers. One of the most important factors affecting the efficiency of the supply chain is the bullwhip effect. This effect influences the whole profitability of the supply chain and the first step to control the bullwhip effect is to measure it. Researchers have referred to different methods to measure the bullwhip effect, but if the data is incomplete, inconsistent, uncertain or unclear, then it is impossible to use them. In this paper, we create a model which uses Fuzzy Inference System (FIS). The flexibility of the proposed model allow managers to estimate the bullwhip effect which enable them to take action reduce this effect.

Correlation of Cultural Capital with Self-development Attitude: A Case Study of Health Care Network in Darehshahr, Iran

Abstract: 1 Assistant Professor, Health Services Management Department, School of Management and Social Science, Islamic Azad University, North Tehran Branch, Tehran, Iran 2 MSc, Department of Health Services Management, School of Management and Social Science, Islamic Azad University, North Tehran Branch, Tehran, Iran 3 Associate Professor, Health Services Management Department, School of Management and Social Science, Islamic Azad University, North Tehran Branch, Tehran, Iran * Corresponding author: Mehrnoosh Jafari, Assistant Professor, Health Services Management Department, School of Management and Social Science, Islamic Azad University, North Tehran Branch, Tehran, Iran. E-mail: mehr_j134@yahoo.com Received: 13 Nov 2016 Accepted: 06 May 2018

Retention time prediction of polycyclic aromatic hydrocarbons in gas chromatography–mass spectrometry using QSPR based on random forests and artificial neural network

Abstract: In this study, a quantitative structure–property relationship (QSPR) was proposed using the random forests (RF) and artificial neural network (ANN) for determining the retention time (RT) of 123 polycyclic aromatic hydrocarbons (PAHs) in tire fire products. The data containing the RT of the PAHs were obtained by gas chromatography–mass spectrometry (GC–MS) method. The optimum number of trees (nt) and the number of randomly selected variables to split each node (m) related to the RF model were found 100 and 101, respectively. Different algorithms of ANN were studied, and Levenberg–Marquardt (LM) algorithm with a minimum mean square error (MSE) was selected as the best algorithm. Also, leave-one-out cross-validation (LOOCV), including correlation determination (R2), standard error of prediction (SEP), mean absolute error (MAE), relative error prediction (REP), mean squared error (MSE), mean relative error (MRE), and predicted residual sum of squares (PRESS), was applied to investigate the validity of statistical models. The R2 of the RF, stepwise regression artificial neural network (SR-ANN), and RF-ANN models were achieved 0.985, 0.983, and 0.959, respectively. An effective model, which does not require experimental steps, was proposed for the RT prediction of new compounds.

3D CFD Modeling and Optimization of a Cylindrical Porous Bed Reactor for Hydrogen Production using Steam Reforming of Methane

Abstract: Steam Reforming of Methane, which converts natural gas into products with higher economic value in the presence of a suitable catalyst bed reformer, is the most economical method for hydrogen production in petroleum refineries. This study focuses on developing a Computational Fluid Dynamics (CFD) model of a steam methane reformer. To this purpose, a steady-state heterogeneous 3 Dimensional model that was composed of mass, species, momentum, and energy balances was developed. It compares two different geometrical porous bed reformers with different heating tube configurations for better heat transfer and reforming. Effects of heating tubes inlet temperature, the ratio of inlet CH4/H2O, and the configuration of the heating tube are studied and optimized. The results show that conversion of methane will be promoted by increasing inlet temperature of the heating tube as well as the number of heating tubes in the reformer when CH4/H2O ratio is about 0.2. In this platform, the conversion of methane is not affected by the porosity below 0.35. Also, the simulations results are shown to be in agreement with typical data reported in the literature. So, this study can be used to develop industrial natural gas reformers.

catena-Poly[[[(2,2'-bipyridine-κN,N')(dimethyl sulfoxide-κO)(nitrato-κO,O')bis-muth(III)]-μ-5-carb-oxy-benzene-1,3-dicarboxyl-ato-κO,O:O,O] dimethyl sulfoxide monosolvate].

Abstract: The polymeric title compound, {[Bi(C9H4O6)(NO3)(C10H8N2)(C2H6OS)]·C2H6OS}n, was obtained by the reaction of bis­muth(III) nitrate, bipyridine (bpy) and 1,3,5-benzene­tricarb­oxy­lic acid (H3BTC). The BiIII ion is coordinated in a distorted tricapped trigonal-prismatic geometry, defined by two N atoms of the bipy ligand, four O atoms of two HBTC2− anions, two O atoms of a nitrate anion and one O atom of a dimethyl sulfoxide ligand. The crystal packing is stabilized by O—H⋯O and C—H⋯O hydrogen bonds. The S atom of the non-coordinating dimethyl sulfoxide mol­ecule is disordered over two sets of sites with refined site-occupancies of 0.430 (19) and 0.570 (19).